load libraries

load the FW functions


### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#

fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  #rankplot <- gather(wideplotmat, party, zetarank, 1:ncol(zetarankmat))
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F, max.overlaps = Inf) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
#    labs(x="Terms used more frequently overall →", y="Terms used more frequently by group →",  title=title, subtitle=subtitle , caption = caption) 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

options(ggrepel.max.overlaps = Inf)

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}

Compare NYT Before and After “extremist” and other query terms

Load and clean the data

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}
transformation drops documentstransformation drops documentstransformation drops documentstransformation drops documentstransformation drops documents

Calculate FW.

[1] 29844 14891
Document-feature matrix of: 6 documents, 14,891 features (100.0% sparse).
       features
docs    us struggl uptick american plot attack past month last week
  text1  1       1      1        1    1      0    0     0    0    0
  text2  0       0      0        0    0      1    1     1    1    1
  text3  0       0      0        0    0      0    0     0    0    0
  text4  0       0      0        0    0      0    0     0    0    0
  text5  0       0      0        0    0      0    0     0    0    0
  text6  0       0      0        0    0      0    0     0    0    0
[ reached max_nfeat ... 14,881 more features ]
Removing features occurring: 
  - fewer than 4 times: 10,569
  Total features removed: 10,569 (71.1%).
[1] 29844  4289

Get and show the top words per group by zeta.

library(knitr)
fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
kable(fwkeys.extrem)
Context.after Context.before
group islam
hussein palestinian
attack war
parti iraqi
elect suspect
leader crack
jihad jemaah
milit crackdown
movement main
said alleg
guerrilla support
network evid
regim albanian
armi sept
politician prodemocraci
organ muslim
outsid megawati
univers battl
bomb prevent
rocket kosovo

Plot: Before in Blue, After in Red

p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"))
p.fw.extrem

Calculate by query item/search term


extrem_NYT.dfm.long$Query.item <- as.factor(extrem_NYT.dfm.long$Query.item)

top_n <-as.data.frame(sort(table(extrem_NYT.dfm.long$Query.item), decreasing = TRUE)[1:5]) 
dim(top_n)
[1] 5 2
colnames(top_n) <- c('term', 'Freq')
top_n

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

r <- sum(length(extrem_dtm_topn_keep))

extrem_dtm_topn_keep <- extrem_dtm_topn_keep[-c(1, r)]

fw.query_item <- fwgroups(extrem_dtm_topn_keep,groups = topn_terms$Query.item)
fwkeys.query_item <- fw.keys(fw.query_item, n.keys=15)
kable(fwkeys.query_item)
Islamic militants opposition Saddam terrorist
milit islam parti hussein attack
hama palestinian democrat iraqi organ
jihad kill leader iraq sept
group isra parliament presid unit
movement suspect elect regim state
extremist gaza vote saddam activ
front israel opposit baghdad alqaida
insurg attack lawmak un act
radic kashmir polit kuwait network
republ taliban main captur group
law arafat candid war laden
fundamentalist forc coalit oust bin
revolut pakistani conserv weapon suspect
somalia fire allianc bush connect
hardlin pakistan protest us financ

rm(r)
rm(extrem_dtm_topn)

p.fw.query_item <- fw.ggplot.groups(fw.query_item,sizescale=2,max.words=50,max.countrank=400)
p.fw.query_item


extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit
extrem_dtm_topn$Context <- extrem_NYT.dfm.long$Context

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep[grep("before",extrem_dtm_topn_keep$Context),]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep[grep("after", extrem_dtm_topn_keep$Context),]

rr <-dim(topn_terms)[1]
r <- sum(length(extrem_dtm_topn_keep_before))
topn_terms_before <- topn_terms[seq(1,rr,2),]
topn_terms_after <- topn_terms[seq(2,rr,2),]

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep_before[-c(1, r-1, r)]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep_after[-c(1, r-1, r)]

rm(rr)
rm(r)
rm(extrem_dtm_topn)

#############################################
fw.query_item_before <- fwgroups(extrem_dtm_topn_keep_before,groups = topn_terms_before$Query.item)
fwkeys.query_item_before <- fw.keys(fw.query_item_before, n.keys=15)
kable(fwkeys.query_item_before)
Islamic militants opposition Saddam terrorist
milit islam main iraqi sept
organ palestinian elect presid alqaida
strict kill polit iraq bin
radic suspect parliament war laden
somalia attack opposit saddam involv
hama taliban parti oust unit
secular isra minist baghdad suspect
hardlin muslim vote fall link
iran gaza democrat bush state
turkey armi prime hussein consid
extremist troop strong captur connect
malaysia israel percent regim appar
suprem pakistan despit usl osama
gaza forc poll former charg
fundamentalist arafat voic death list

p.fw.query_item_before <- fw.ggplot.groups(fw.query_item_before,sizescale=2,max.words=50,max.countrank=400,
                                           colorpalette = c('red', 'red','red','red','red'))
p.fw.query_item_before


#############################################
fw.query_item_after <- fwgroups(extrem_dtm_topn_keep_after,groups = topn_terms_after$Query.item)
fwkeys.query_item_after <- fw.keys(fw.query_item_after, n.keys=15)
kable(fwkeys.query_item_after)
Islamic militants opposition Saddam terrorist
milit isra parti hussein attack
group israel leader regim organ
jihad kashmir democrat iraq state
hama gaza vote iraqi unit
movement palestinian parliament un activ
law kill lawmak weapon group
insurg attack protest saddam act
extremist region elect son network
front southern candid captur bomb
revolut fire politician kuwait suspect
republ rocket coalit mass threat
court pakistani social baghdad financ
fundamentalist fight conserv us sept
news border win resolut link
agenc area opposit palac cell

p.fw.query_item_after <- fw.ggplot.groups(fw.query_item_after,sizescale=2,max.words=50,max.countrank=400,
                                           colorpalette = c('blue','blue','blue', 'blue','blue'))
p.fw.query_item_after

NA
NA

Calculate Parts of speech by before and after

Calculate FW and keys

### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html

library(udpipe)

ud_model <- udpipe_download_model(language = "english")
ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

A few barchart functions

Bar Charts from Functions Above

Cooccurences

Cooccurences (part 2)


Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  print(y[1:25,])
}


Corrs(x_odd.before)
Corrs(x_even.after)

home

---
title: "Extrem(ist +) Fightin' Words"
author: "Breanna E. Green"
subtitle: practicing
output:
  html_document:
    toc: yes
    df_print: paged
  html_notebook:
    code_folding: show
    df_print: paged
    highlight: tango
    theme: united
    toc: yes
---

## load libraries

```{r, echo=FALSE}
library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
```

## load the FW functions

```{r}

### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#

fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  #rankplot <- gather(wideplotmat, party, zetarank, 1:ncol(zetarankmat))
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F, max.overlaps = Inf) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
#    labs(x="Terms used more frequently overall →", y="Terms used more frequently by group →",  title=title, subtitle=subtitle , caption = caption) 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

options(ggrepel.max.overlaps = Inf)

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}
```


## Compare NYT Before and After "extremist" and other query terms

Load and clean the data

  * to string & lower text
  * pivot to long format
  * apply text_cleaner to one column "context.text"

```{r,  results='asis'}
text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

```

```{r, echo=FALSE}
extrem_NYT.dfm_all <-read.delim("~/Documents/GitHub/70corr_extremist_9410_NYT.txt", header=TRUE, sep="\t")
set.seed(2217)
extrem_NYT.dfm <- as.data.frame(sample_n(extrem_NYT.dfm_all, 15000))
rm(extrem_NYT.dfm_all)

extrem_NYT.dfm$Context.before = lapply(extrem_NYT.dfm$Context.before, toString)
extrem_NYT.dfm$Context.before = lapply(extrem_NYT.dfm$Context.before, tolower)

extrem_NYT.dfm$Context.after = lapply(extrem_NYT.dfm$Context.after, toString)
extrem_NYT.dfm$Context.after = lapply(extrem_NYT.dfm$Context.after, tolower)

extrem_NYT.dfm <- extrem_NYT.dfm %>% distinct(Context.before, .keep_all = TRUE)

extrem_NYT.dfm.long <- pivot_longer(extrem_NYT.dfm, cols=c(Context.before, Context.after), names_to = "Context", values_to = "context.text")

extrem_NYT.dfm.long$Context <- as.factor(extrem_NYT.dfm.long$Context)

extremecorpus <-text_cleaner(extrem_NYT.dfm.long$context.text)

```


Calculate FW.

```{r, echo=FALSE}
library(quanteda)

e <- dfm(extremecorpus$content)
dim(e)
head(e)

e <- dfm_select(e, pattern = stopwords("english"), selection = "remove")
e <- dfm_select(e, min_nchar = 2)
e <- dfm_trim(e, min_termfreq = 4, min_docfreq = .05, verbose=TRUE)

dim(e)
# sparsity(e)

extrem_dtm <- convert(e, to='data.frame')

extrem_dtm <- extrem_dtm[-c(1)]

w <- which( sapply(extrem_dtm, class ) == 'character' )

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)

```


Get and show the top words per group by zeta.

```{r echo=TRUE, results="asis"}
library(knitr)
fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
kable(fwkeys.extrem)
```

Plot: Before in Blue, After in Red

```{r, fig.height=5, fig.width=4}
p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"))
p.fw.extrem
```

## Calculate by query item/search term

```{r}

extrem_NYT.dfm.long$Query.item <- as.factor(extrem_NYT.dfm.long$Query.item)

top_n <-as.data.frame(sort(table(extrem_NYT.dfm.long$Query.item), decreasing = TRUE)[1:5]) 
dim(top_n)
colnames(top_n) <- c('term', 'Freq')
top_n

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

r <- sum(length(extrem_dtm_topn_keep))

extrem_dtm_topn_keep <- extrem_dtm_topn_keep[-c(1, r)]

fw.query_item <- fwgroups(extrem_dtm_topn_keep,groups = topn_terms$Query.item)
fwkeys.query_item <- fw.keys(fw.query_item, n.keys=15)
kable(fwkeys.query_item)

rm(r)
rm(extrem_dtm_topn)

p.fw.query_item <- fw.ggplot.groups(fw.query_item,sizescale=2,max.words=50,max.countrank=400)
p.fw.query_item

```



```{r, echo=TRUE, results='asis'}

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit
extrem_dtm_topn$Context <- extrem_NYT.dfm.long$Context

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep[grep("before",extrem_dtm_topn_keep$Context),]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep[grep("after", extrem_dtm_topn_keep$Context),]

rr <-dim(topn_terms)[1]
r <- sum(length(extrem_dtm_topn_keep_before))
topn_terms_before <- topn_terms[seq(1,rr,2),]
topn_terms_after <- topn_terms[seq(2,rr,2),]

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep_before[-c(1, r-1, r)]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep_after[-c(1, r-1, r)]

rm(rr)
rm(r)
rm(extrem_dtm_topn)

#############################################
fw.query_item_before <- fwgroups(extrem_dtm_topn_keep_before,groups = topn_terms_before$Query.item)
fwkeys.query_item_before <- fw.keys(fw.query_item_before, n.keys=15)
kable(fwkeys.query_item_before)

p.fw.query_item_before <- fw.ggplot.groups(fw.query_item_before,sizescale=2,max.words=50,max.countrank=400,
                                           colorpalette = c('red', 'red','red','red','red'))
p.fw.query_item_before

#############################################
fw.query_item_after <- fwgroups(extrem_dtm_topn_keep_after,groups = topn_terms_after$Query.item)
fwkeys.query_item_after <- fw.keys(fw.query_item_after, n.keys=15)
kable(fwkeys.query_item_after)

p.fw.query_item_after <- fw.ggplot.groups(fw.query_item_after,sizescale=2,max.words=50,max.countrank=400,
                                           colorpalette = c('blue','blue','blue', 'blue','blue'))
p.fw.query_item_after


```



## Calculate Parts of speech by before and after

Calculate FW and keys
```{r, results='hide'}
### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html

library(udpipe)

ud_model <- udpipe_download_model(language = "english")

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]


```


A few barchart functions

```{r, echo=FALSE, results='hide'}
library(lattice)
library(latticeExtra)
library(ggplot2)
library(plotly)
library(pdp)
library(patchwork)


## UNIVERSAL PoS
UPOS_barchart <- function(df1, df2){
  stats1 <- txt_freq(df1$upos)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- txt_freq(df2$upos)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = stats1, col = "cadetblue", 
        main = "UPOS (Universal Parts of Speech)\n frequency of occurrence: BEFORE vs AFTER", 
         xlab = "Freq"), 
    barchart(key ~ freq, data = stats2, col =  'skyblue',
         xlab = "Freq"))
}



## NOUNS
NOUNS_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("NOUN")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("NOUN")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring nouns: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
            xlab = "Freq"))
}

## ADJECTIVES
ADJ_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("ADJ")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("ADJ")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring adjectives: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
         xlab = "Freq"))
}

## Using RAKE to find keywords
RAKE_KW_barchart <- function(df1,df2){
  
  stats1 <- keywords_rake(x = df1, term = "lemma", group = "doc_id", 
                         relevant = df1$upos %in% c("NOUN", "ADJ"))
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  stats2 <- keywords_rake(x = df2, term = "lemma", group = "doc_id", 
                         relevant = df2$upos %in% c("NOUN", "ADJ"))
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  
  c(barchart(key ~ rake, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by RAKE: BEFORE vs AFTER", 
           xlab = "Rake"),
    barchart(key ~ rake, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "Rake"))
}

## Using Pointwise Mutual Information Collocations
PWI_barchart <- function(df1, df2){
  
  df1$word <- tolower(df1$token)
  stats1 <- keywords_collocation(x = df1, term = "word", group = "doc_id")
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$word <- tolower(df2$token)
  stats2 <- keywords_collocation(x = df2, term = "word", group = "doc_id")
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ pmi, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by PMI Collocation: BEFORE vs AFTER", 
           xlab = "PMI (Pointwise Mutual Information)"),
      barchart(key ~ pmi, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "PMI (Pointwise Mutual Information)"))
}

## Using a sequence of POS tags (noun phrases / verb phrases)
POS_barchart <- function(df1, df2){
  
  df1$phrase_tag <- as_phrasemachine(df1$upos, type = "upos")
  stats1 <- keywords_phrases(x = df1$phrase_tag, term = tolower(df1$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats1 <- subset(stats1, ngram > 1 & freq > 3)
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$phrase_tag <- as_phrasemachine(df2$upos, type = "upos")
  stats2 <- keywords_phrases(x = df2$phrase_tag, term = tolower(df2$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats2 <- subset(stats2, ngram > 1 & freq > 3)
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Keywords - simple noun phrases: BEFORE vs AFTER", xlab = "Frequency"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
               xlab = "Frequency"))
}
```


## Bar Charts from Functions Above

```{r, echo=FALSE, results='asis'}

UPOS_barchart(x_odd.before, x_even.after)
NOUNS_barchart(x_odd.before, x_even.after)
ADJ_barchart(x_odd.before, x_even.after)
RAKE_KW_barchart(x_odd.before, x_even.after)
PWI_barchart(x_odd.before, x_even.after)
POS_barchart(x_odd.before, x_even.after)

```


## Cooccurences

```{r, echo=FALSE}

CO_OC_noun_adj_same_sent.before <- function(df1){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df1, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 40)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective")
  
}

CO_OC_noun_adj_same_sent.after <- function(df2){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df2, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 40)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective")
  
}


CO_OC_noun_adj_same_sent.before(x_odd.before)
CO_OC_noun_adj_same_sent.after(x_even.after)


##########################################

CO_OC_noun_adj_following.before <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 15)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: BEFORE", subtitle = "Nouns & Adjective")
}

CO_OC_noun_adj_following.after <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 15)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "skyblue") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: AFTER", subtitle = "Nouns & Adjective")
}


CO_OC_noun_adj_following.before(x_odd.before)
CO_OC_noun_adj_following.after(x_even.after)

```

## Cooccurences (part 2)
```{r}

Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  print(y[1:25,])
}


Corrs(x_odd.before)
Corrs(x_even.after)
```


```{r final}
```

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